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        <span>查看单独数据下的准确率</span>
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        <h1 id="查看单独数据下的准确率"><a href="#查看单独数据下的准确率" class="headerlink" title="查看单独数据下的准确率"></a>查看单独数据下的准确率</h1><p>查看每个分期下的特征重要度，并选取均值，然后取18个数据的均值</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/8/24</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 忽略警告</span></span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 将2345期的组合放到1个list中</span></span><br><span class="line">slpdb_stage = []</span><br><span class="line">key_word = []</span><br><span class="line"><span class="comment"># 读取2345期的睡眠结果</span></span><br><span class="line"><span class="keyword">for</span> index <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">2</span>, <span class="number">6</span>):</span><br><span class="line">    df = pd.read_excel(<span class="string">&#x27;E:/8-23 feature section and importance/slpdb_feature_mean_stage&#x27;</span> + <span class="string">&#x27;%d&#x27;</span> % index + <span class="string">&#x27;.xlsx&#x27;</span>)</span><br><span class="line">    key_word = df.keys()[<span class="number">1</span>:]</span><br><span class="line">    data = np.array(df)</span><br><span class="line">    all_feature = []</span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">1</span>, <span class="number">103</span>):</span><br><span class="line">        feature_mean = []</span><br><span class="line">        <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">18</span>):</span><br><span class="line">            feature_mean.append(data[j][i])</span><br><span class="line">        all_feature.append(np.array(feature_mean).mean())</span><br><span class="line">    slpdb_stage.append(all_feature)</span><br></pre></td></tr></table></figure>





<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/8/24</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 忽略警告</span></span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 将2345期的组合放到1个list中</span></span><br><span class="line">slpdb_stage = []</span><br><span class="line">key_word = []</span><br><span class="line"><span class="comment"># 读取2345期的睡眠结果</span></span><br><span class="line"><span class="keyword">for</span> index <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">2</span>, <span class="number">6</span>):</span><br><span class="line">    df = pd.read_excel(<span class="string">&#x27;E:/8-23 feature section and importance/slpdb_feature_mean_stage&#x27;</span> + <span class="string">&#x27;%d&#x27;</span> % index + <span class="string">&#x27;.xlsx&#x27;</span>)</span><br><span class="line">    key_word = df.keys()[<span class="number">1</span>:]</span><br><span class="line">    data = np.array(df)</span><br><span class="line">    all_feature = [np.array([data[j][i] <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">18</span>)]).mean() <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">1</span>, <span class="number">103</span>)]</span><br><span class="line">    slpdb_stage.append(all_feature)</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">feature_name = []</span><br><span class="line"><span class="keyword">for</span> i_x <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">4</span>):</span><br><span class="line">    dict_data = <span class="built_in">dict</span>(<span class="built_in">zip</span>(key_word, slpdb_stage[i_x]))</span><br><span class="line">    <span class="comment"># 将字典中的值按照降序排序</span></span><br><span class="line">    <span class="comment"># sorted, reverse -- 排序规则，reverse = True 降序 ， reverse = False 升序（默认）,降序排列</span></span><br><span class="line">    sort_score = <span class="built_in">sorted</span>(<span class="built_in">zip</span>(dict_data.values(), dict_data.keys()), reverse=<span class="literal">True</span>)</span><br><span class="line">    <span class="comment"># 得到30个特征的关键词</span></span><br><span class="line">    F1_keys = [sort_score[i][<span class="number">1</span>] <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">30</span>)]</span><br><span class="line">    feature_name.append(F1_keys)</span><br><span class="line"></span><br><span class="line">feature_names = pd.DataFrame(feature_name, index=[<span class="string">&#x27;stage_2&#x27;</span>, <span class="string">&#x27;stage_3&#x27;</span>, <span class="string">&#x27;stage_4&#x27;</span>, <span class="string">&#x27;stage_5&#x27;</span>], columns=np.arange(<span class="number">1</span>, <span class="number">31</span>)).T</span><br><span class="line">feature_names.to_excel(<span class="string">&#x27;slpdb的特征贡献度特征排序.xlsx&#x27;</span>)</span><br><span class="line"></span><br><span class="line"></span><br></pre></td></tr></table></figure>



<h1 id="查看两个数据库的单人情况"><a href="#查看两个数据库的单人情况" class="headerlink" title="查看两个数据库的单人情况"></a>查看两个数据库的单人情况</h1><p>首先生成对应的特征组合数据</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 忽略警告</span></span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 读取2345期的睡眠结果</span></span><br><span class="line"><span class="keyword">for</span> index <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">2</span>, <span class="number">6</span>):</span><br><span class="line">    df = pd.read_excel(<span class="string">&#x27;E:/8-23 feature section and importance/slpdb_feature_acr_stage&#x27;</span> + <span class="string">&#x27;%d&#x27;</span> % index + <span class="string">&#x27;.xlsx&#x27;</span>).T</span><br><span class="line">    <span class="comment"># 读取18个数据的平均准确率</span></span><br><span class="line">    F_mean = [[np.array([<span class="built_in">eval</span>(df[k][i])[num] <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">50</span>)]).mean() <span class="keyword">for</span> num <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">25</span>)] <span class="keyword">for</span> k <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">18</span>)]</span><br><span class="line">    slpdb_singel = pd.DataFrame(F_mean)</span><br><span class="line">    slpdb_singel.to_excel(<span class="string">&#x27;slpdb_stage_&#x27;</span> + <span class="string">&#x27;%d&#x27;</span> % index + <span class="string">&#x27;.xlsx&#x27;</span>)</span><br><span class="line"></span><br></pre></td></tr></table></figure>

<p>查看生成的4个阶段，在同一个数据库中，差距变化并不大</p>
<p>因此现在选择初始的5个特征作为比较，看每个数据在不同周期下的情况</p>
<table>
<thead>
<tr>
<th></th>
<th><strong>stage_2</strong></th>
<th><strong>stage_3</strong></th>
<th><strong>stage_4</strong></th>
<th><strong>stage_5</strong></th>
<th><strong>AHI</strong></th>
<th><strong>Seff(%)</strong></th>
</tr>
</thead>
<tbody><tr>
<td>slp01a</td>
<td>97.59%</td>
<td>95.80%</td>
<td>83.88%</td>
<td>82.55%</td>
<td>17.0</td>
<td>97.82</td>
</tr>
<tr>
<td>slp01b</td>
<td>86.95%</td>
<td>80.95%</td>
<td>81.58%</td>
<td>76.67%</td>
<td>22.3</td>
<td>48.42</td>
</tr>
<tr>
<td>slp02a</td>
<td>94.27%</td>
<td>89.12%</td>
<td>85.77%</td>
<td>83.47%</td>
<td>34.0</td>
<td>85.10</td>
</tr>
<tr>
<td>slp02b</td>
<td>89.15%</td>
<td>85.10%</td>
<td>85.79%</td>
<td>84.00%</td>
<td>22.2</td>
<td>60.62</td>
</tr>
<tr>
<td>slp03</td>
<td>88.86%</td>
<td>81.59%</td>
<td>77.32%</td>
<td>70.04%</td>
<td>43.0</td>
<td>83.19</td>
</tr>
<tr>
<td>slp04</td>
<td>91.11%</td>
<td>89.93%</td>
<td>85.92%</td>
<td>83.01%</td>
<td>59.8</td>
<td>78.14</td>
</tr>
<tr>
<td>slp14</td>
<td>81.29%</td>
<td>79.86%</td>
<td>79.11%</td>
<td>65.33%</td>
<td>30.7</td>
<td>55.05</td>
</tr>
<tr>
<td>slp16</td>
<td>84.72%</td>
<td>83.80%</td>
<td>82.58%</td>
<td>80.89%</td>
<td>53.1</td>
<td>55.20</td>
</tr>
<tr>
<td>slp32</td>
<td>92.84%</td>
<td>92.63%</td>
<td>81.66%</td>
<td>80.86%</td>
<td>22.1</td>
<td>39.11</td>
</tr>
<tr>
<td>slp37</td>
<td>94.72%</td>
<td>94.23%</td>
<td>94.07%</td>
<td>92.42%</td>
<td>100.8</td>
<td>89.37</td>
</tr>
<tr>
<td>slp41</td>
<td>83.87%</td>
<td>80.64%</td>
<td>80.39%</td>
<td>62.35%</td>
<td>60 [2]</td>
<td>71.65</td>
</tr>
<tr>
<td>slp45</td>
<td>92.92%</td>
<td>88.30%</td>
<td>80.58%</td>
<td>76.06%</td>
<td>5 [2]</td>
<td>85.05</td>
</tr>
<tr>
<td>slp48</td>
<td>90.36%</td>
<td>88.83%</td>
<td>89.14%</td>
<td>66.04%</td>
<td>46.8</td>
<td>72.10</td>
</tr>
<tr>
<td>slp59</td>
<td>86.49%</td>
<td>84.24%</td>
<td>77.87%</td>
<td>72.43%</td>
<td>55.3</td>
<td>69.80</td>
</tr>
<tr>
<td>slp60</td>
<td>87.78%</td>
<td>84.57%</td>
<td>85.07%</td>
<td>78.56%</td>
<td>59.2</td>
<td>59.23</td>
</tr>
<tr>
<td>slp61</td>
<td>89.54%</td>
<td>87.57%</td>
<td>80.29%</td>
<td>73.15%</td>
<td>41.2</td>
<td>83.22</td>
</tr>
<tr>
<td>slp66</td>
<td>87.94%</td>
<td>88.20%</td>
<td>87.04%</td>
<td>73.16%</td>
<td>65.5</td>
<td>61.07</td>
</tr>
<tr>
<td><strong>slp67x</strong></td>
<td><strong>79.95%</strong></td>
<td><strong>79.44%</strong></td>
<td><strong>80.09%</strong></td>
<td><strong>69.72%</strong></td>
<td>0.7</td>
<td>54.55</td>
</tr>
</tbody></table>
<table>
<thead>
<tr>
<th></th>
<th><strong>stage_2</strong></th>
<th><strong>stage_3</strong></th>
<th><strong>stage_4</strong></th>
<th><strong>stage_5</strong></th>
<th>PSG AHI</th>
<th>Seff (%)</th>
</tr>
</thead>
<tbody><tr>
<td>UCDDB002</td>
<td>90.15%</td>
<td>84.77%</td>
<td>76.27%</td>
<td>66.92%</td>
<td>23</td>
<td>84.96</td>
</tr>
<tr>
<td>UCDDB003</td>
<td>95.50%</td>
<td>91.20%</td>
<td>85.08%</td>
<td>80.12%</td>
<td>51</td>
<td>81.97</td>
</tr>
<tr>
<td>UCDDB005</td>
<td>87.31%</td>
<td>82.83%</td>
<td>81.25%</td>
<td>73.46%</td>
<td>13</td>
<td>65.86</td>
</tr>
<tr>
<td>UCDDB006</td>
<td>95.00%</td>
<td>90.21%</td>
<td>82.68%</td>
<td>77.42%</td>
<td>31</td>
<td>92.79</td>
</tr>
<tr>
<td>UCDDB007</td>
<td>92.66%</td>
<td>87.80%</td>
<td>81.70%</td>
<td>76.85%</td>
<td>12</td>
<td>90.52</td>
</tr>
<tr>
<td>UCDDB008</td>
<td>88.47%</td>
<td>85.11%</td>
<td>80.01%</td>
<td>71.42%</td>
<td>5</td>
<td>73.83</td>
</tr>
<tr>
<td>UCDDB009</td>
<td>86.84%</td>
<td>86.61%</td>
<td>76.91%</td>
<td>70.63%</td>
<td>12</td>
<td>79.98</td>
</tr>
<tr>
<td>UCDDB010</td>
<td>93.61%</td>
<td>86.52%</td>
<td>82.15%</td>
<td>76.10%</td>
<td>34</td>
<td>92.52</td>
</tr>
<tr>
<td>UCDDB011</td>
<td>91.37%</td>
<td>88.07%</td>
<td>80.90%</td>
<td>78.05%</td>
<td>8</td>
<td>61.12</td>
</tr>
<tr>
<td>UCDDB012</td>
<td>91.60%</td>
<td>84.68%</td>
<td>80.43%</td>
<td>75.52%</td>
<td>25</td>
<td>86.05</td>
</tr>
<tr>
<td>UCDDB013</td>
<td>88.38%</td>
<td>85.77%</td>
<td>84.61%</td>
<td>81.25%</td>
<td>16</td>
<td>61.75</td>
</tr>
<tr>
<td>UCDDB014</td>
<td>87.39%</td>
<td>84.66%</td>
<td>85.00%</td>
<td>74.87%</td>
<td>36</td>
<td>79.82</td>
</tr>
<tr>
<td>UCDDB015</td>
<td>83.48%</td>
<td>79.19%</td>
<td>78.02%</td>
<td>70.76%</td>
<td>6</td>
<td>80.25</td>
</tr>
<tr>
<td>UCDDB017</td>
<td>93.46%</td>
<td>88.16%</td>
<td>81.74%</td>
<td>80.11%</td>
<td>12</td>
<td>88.17</td>
</tr>
<tr>
<td><strong>UCDDB018</strong></td>
<td><strong>92.25%</strong></td>
<td><strong>89.76%</strong></td>
<td><strong>90.24%</strong></td>
<td><strong>87.82%</strong></td>
<td>2</td>
<td>60.91</td>
</tr>
<tr>
<td>UCDDB019</td>
<td>94.89%</td>
<td>84.90%</td>
<td>82.22%</td>
<td>80.19%</td>
<td>16</td>
<td>92.74</td>
</tr>
<tr>
<td>UCDDB020</td>
<td>84.75%</td>
<td>82.27%</td>
<td>80.90%</td>
<td>75.78%</td>
<td>15</td>
<td>78.14</td>
</tr>
<tr>
<td>UCDDB021</td>
<td>91.93%</td>
<td>87.16%</td>
<td>83.76%</td>
<td>79.42%</td>
<td>13</td>
<td>84.77</td>
</tr>
<tr>
<td>UCDDB022</td>
<td>87.14%</td>
<td>86.52%</td>
<td>85.84%</td>
<td>84.08%</td>
<td>7</td>
<td>59.20</td>
</tr>
<tr>
<td>UCDDB023</td>
<td>81.09%</td>
<td>79.60%</td>
<td>76.65%</td>
<td>64.41%</td>
<td>39</td>
<td>66.19</td>
</tr>
<tr>
<td>UCDDB024</td>
<td>91.32%</td>
<td>86.76%</td>
<td>83.42%</td>
<td>79.03%</td>
<td>24</td>
<td>83.50</td>
</tr>
<tr>
<td>UCDDB025</td>
<td>78.15%</td>
<td>75.19%</td>
<td>74.87%</td>
<td>58.10%</td>
<td>91</td>
<td>78.00</td>
</tr>
<tr>
<td>UCDDB026</td>
<td>95.52%</td>
<td>85.37%</td>
<td>79.05%</td>
<td>74.18%</td>
<td>14</td>
<td>87.30</td>
</tr>
<tr>
<td>UCDDB027</td>
<td>92.88%</td>
<td>85.71%</td>
<td>86.15%</td>
<td>80.86%</td>
<td>55</td>
<td>86.85</td>
</tr>
<tr>
<td>UCDDB028</td>
<td>73.49%</td>
<td>64.52%</td>
<td>62.79%</td>
<td>55.63%</td>
<td>46</td>
<td>69.20</td>
</tr>
</tbody></table>
<p>利用AHI和SEFF进行分类别，看下平均的</p>
<p><img src="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1598265807368&di=de5fc4ce35b04ffbc8ea8699648516a7&imgtype=0&src=http://i0.hdslb.com/bfs/article/f19c090f678c8e472ad62affb63457d976163339.jpg"></p>

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